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International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),
ISSN 0976 – 6359(Online) Volume 2, Issue 2, August- December (2011), © IAEME
126
EVALUATION OF MACHINABILITY CHARACTERISTICS OF
INDUSTRIAL CERAMIC COATINGS USING GENETIC
PROGRAMMING BASED APPROACH
Mohammed Yunus1, Dr. J. Fazlur Rahman
2 and S.Ferozkhan
3
1. Research scholar, Anna University of Technology Coimbatore
Professor, Department of Mechanical Engineering H.K.B.K.C.E.,
Bangalore, India.
Mobile: +919141369124
2. Supervisor, Anna University of Technology Coimbatore
Professor Emeritus, Department of MechanicalEngineering
H.K.B.K.C.E., Bangalore, India.
Mobile: +919845350742
3. Lecturer, Department of Mechanical Engineering,
H.K.B.K.C.E., Bangalore, India.
ABSTRACT
Ceramic coated components have the advantage features of both metal and
ceramics, i.e. good toughness, high hardness and wear resistance. Despite their
outstanding characteristics, ceramic materials are not used in many cases due to
high cost of machining. A major drawback to engineering applications of
ceramic is due to their brittleness and fracture toughness, which makes them
difficult and costly to machine. In order to study the precision machining
processes of ceramics like grinding and lapping, number of trial experiments
were conducted to find out the influence of various cutting parameters on
surface quality production as well as grinding and lapping performances.
Surface grinding of ceramic coating materials was done on samples specimens
coated with Alumina (Al2O3), Alumina-Titania (Al2O3-TiO2) and Partially
stabilized zirconia (PSZ )using diamond and CBN (cubic boron nitride)
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ISSN 0976 – 6340 (Print) ISSN 0976 – 6359 (Online)
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grinding wheels. Based on the experimental results, the influence of cutting
parameters, namely, cutting force, surface roughness and bearing area
characteristics were evaluated and optimum machining conditions have been
suggested for better performance in precision machining of ceramic coating
materials.
Genetic programming (GP) has proved to be a highly versatile and useful tool
for identifying relationships in data for which a more precise theoretical
construct is unavailable. The prediction of machinability of ceramic oxide
coatings, depending on input parameters (type of grinding wheels, type of
coatings, depth of cut, grinding speed, lapping duration etc.), was made by
means of genetic programming using data values on the outputs ( normal and
tangential forces, surface roughness Ra, Rt etc.) of grinding and lapping
operations of experimental results already made. This paper highlights how one
can use GP technique in the prediction of output parameters. Commercial
Genetic Programming (GP) software-Discipulus is used to derive a
mathematical modelling of relations for various input and output parameters
used in characterisation. A special genetic approach for the modelling of
Machinability characteristics in coated components is proposed on the basis of
a training data set. Different types of genetic models for prediction of different
machinability properties with greater accuracy (less than 1%) were also
proposed by simulated evolution.
Keywords: Evolutionary computation; Genetic programming; Cutting force;
Diamond and CBN grinding wheel; Precision machining; Grinding and
Lapping; Optimizations of Cutting parameters; Precision Machining Processes;
Surface roughness. .
1. INTRODUCTION
With the projected wide spread applications of ceramic coating materials, it
is necessary to develop an appropriate technology for their efficient and cost
effective machining processes [5] & [8-[10 ]. Grinding of ceramics is a difficult
task, as it is generally associated with cracking, splintering and delamination of
surfaces. Conventional processes and tools are not generally suited for the
machining of ceramics. Standard machining tools can be used with
optimization of machining parameters in operating conditions [ 7-10 ]. Various
precision machining techniques that could be adopted for efficient machining
of ceramic materials, namely grinding and lapping processes are studied in
detail for the parametric influence of various cutting parameters of precision
machining of ceramic coating materials [12-13].
Ceramic coated components used in industrial applications, generally
require post treatments like heat treatment and surface finishing by precision
machining [1-5]. Good surface finish and high efficiency in machining to meet
the demands of tight tolerances are generally achieved by grinding, lapping and
polishing like precision machining processes [3].
Grinding of ceramics is a difficult task, as it is generally associated with
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),
ISSN 0976 – 6359(Online) Volume 2, Issue 2, August- December (2011), © IAEME
128
cracking of surface. In order to study the effect of precision machining
processes[14-15], experiments were conducted to check the machining
parameters like surface quality, grinding forces etc. on ceramic coated
components for different machining conditions.
The main object of this study is to evaluate the behavior of A, AT, PSZ,
Super-Z alloy and ZTA ceramic coating materials subjected to different
grinding conditions. The performance was evaluated by measuring grinding
force [14], surface finish [8-9] and bearing area characteristics and also using oil
film retainability characteristics.
1.2 Genetic Programming (GP)
GP works with a large population of candidate programs and uses the
Darwinian principle of “survival of the fittest” to produce successively better
programs for a given problem and evolves a program that predicts the target
output from a data file of inputs and outputs [17-19]. The programs evolved by
GP software – Discipulus [20], in this case Java, C/C++ and assembly
interpreter programs represents a mapping of input to output data. This is done
by Machine Learning that maps a set of input data to known output data. The
aims of using the machine learning technique on engineering problems are to
determine data mining and knowledge discovery. GP provides a significant
benefit in many areas of science and industry [21-25]. The Discipulus GP [20]
system uses AIM Learning Technology. “AIM” stands for “Automatic
Induction of Machine Code”. AIM Learning and Discipulus deal with the
machine learning speed problem. This speed allows the analyst to able to make
many more runs to investigate relationships between data and output, assess
information content of data streams, uncover bad data or outliers, assess time
lag relationships between inputs and outputs, and the like. The evolved models
have been used to develop process prediction or to control algorithms. Hence
GP technology has been selected for the present work.
GP solutions are computer programs that can be easily inspected,
documented, evaluated, and tested. The GP solutions are easy to understand the
nature of the derived relationship between input and output data and to examine
the uncover relationships that were unknown before. Genetic Programming
evolves both the structure and the constants to the solution simultaneously.
Discipulus GP strongly discriminates between relevant input data and inputs
that have no bearing on a solution [20]. In other words, Discipulus performs
input variable selection as a by-product of its learning algorithm.
The following step by step procedure will be implemented for a steady state
GP algorithm [17-19].
1. Initialization of population: Generate an initial population of random
compositions of the functions and terminals of the problem (computer
programs).
2. Fitness evaluation: Execute each program in the population, randomly it
selects some programs and assign it a fitness value according to how well it
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),
ISSN 0976 – 6359(Online) Volume 2, Issue 2, August- December (2011), © IAEME
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solves the problem by mapping input data to output data. Some programs are
selected as best programs (winner programs)
3. Create a new population of computer programs by exchanging parts of the
“best” programs with each other (called crossover).
4. Copy the best existing programs.
5. Create new computer programs by randomly changing each of the
tournament winners to create two new programs mutation.
6. Iterate Until Convergence. Repeat steps two through four until a program is
developed that predicts the behavior sufficiently.
GP has been successfully used to solve problems in a wide range of broad
categories [17-26]:
1. Systems Modelling, Curve Fitting, Data Modelling, and Symbolic
Regression
2. Industrial Process Control
3. Financial Trading, Time Series Prediction and Economic Modelling
4. Optimisation and scheduling
5. Medicine, Biology and Bioinformatics
6. Design
7. Image and Signal processing
8. Entertainment and Computer games
2. EXPERIMENTAL PROCEDURE
Three different commercially available ceramic coating powder materials namely, Alumina (Al2O3), Alumina-Titania (Al2O3-TiO2), Partially Stabilized Zirconia (PSZ) were used for the preparation of coatings [3] &[7]. A 40 KW Sulzer, Metco plasma spray system with 7MB gun is used for this plasma spraying of coatings. Mild steel plates of 50x50x6 mm were used as substrate to spray the ceramic oxides. They were grit blasted, degreased and spray coated with a 50 to 100 microns Ni Cr Alumel bond coat. The above ceramic materials were then plasma sprayed using optimum spray parameters.
2.1 Precision Machining of Ceramic Coating
Grinding: Using diamond and CBN grinding wheels [20] with the surface
grinding trials were conducted with the grinding conditions mentioned below in
table 1. Machining trials were conducted on different ceramic coated
specimens (A, AT and PSZ ceramic coatings).
The main object of this study is to evaluate the behavior of A, AT and PSZ
ceramic coatings subjected to different grinding conditions. The performance
[14] was evaluated by measuring
Table1. Grinding specifications
Type of grinding Surface grinding
Grinding wheel used Diamond of 185 grit size
CBN of 120 grit size
Wheel speed used 5 to 30m/sec
Depth of grinding 10 to 40µm
Work feed 0.5 mm/sec
Table2. Lapping specifications
Type of Machine Flat surface hand lapping
Lapping medium Abrasive and diamond compound paste.
Diamond size 2-10 µ m
Lapping speed 0.2 m/sec
Lapping pressure 0.05 MPa
Lapping time duration 25 minutes
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1. Grinding force ( both normal and tangential forces)
2. Surface finish produced which also includes the bearing area
characteristics
2.2 Force Measurement
The normal grinding force (Fn) and the tangential force (Ft) were measured
using grinding dynamometer [14].The ground samples were measured for
different surface finish parameters such as Ra, Rt, and tp values using Taylor
Hobson’s stylus tracing profilometer.
2.3 Lapping
A circular disc of 50 mm in diameter made of bright steel was coated with
NiCrAlumel bond coat of thickness 75µm and subsequently coated with
different ceramic coating materials namely, Alumina (A), Alumina-Titania
(AT), Partial Stabilized Zirconia (PSZ), Super-Z alloy and ZTA [19-20].
Samples were initially ground to achiev pre lapping finish and then further
lapped under the conditions mentioned above in table 2. The process variables
chosen were lapping time (ranging 5 to 25 minutes) and size of the diamond
abrasives in lapping [5].
The lapped discs were thoroughly cleaned and measurement in respect of
surface finish was made using Taylor Hobson’s surface finish profilometer.
3. Genetic Programming Methodology: Genetic programming can be the most general approach among evolutionary
computation methods in which the coatings subjected to different precision
machining operations and cutting parameters variation adaptation are those
hierarchically organized computer programs whose size and form dynamically
change during simulated evolution. The initial population in GP is obtained by
the creation of random computer programs consisting of available function
genes from set F and available terminal genes from set T. In Genetic
programming modelling, it is necessary to select suitable terminal from set F
and available terminal genes from set f (0)[17-25]. From these, the
evolutionary process will try to build as fit an organism (i.e. mathematical
model) as possible for machinability characteristics prediction. The organisms
consist of both terminal and function genes and have the nature of computer
programs which differ in form and size.
In our case the set of terminal genes f (0) is: f (0) = {Process inputs}.The
selected set of function genes F is: F = {+, -, *, /}, where +,-,*, / are the
mathematical operation of addition, subtraction, multiplication and division.
The quality of the individual organism (i.e. prediction) is found out using
fitness function. In our case, four different functions are used.
Process Inputs: Depth of cut (µm),
Hardness of a wheel,
Hardness of coatings (HV),
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Grinding speed (m/sec),
Toughness (MPa√m)
Thermal conductivity (W/m K)
Thermal Diffusivity (10-7
m2/sec)
Measured Process Outputs f(0) Normal Grinding force Fn (N)
Tangential Grinding force Ft (N)
Surface roughness Ra (µm) Table 3. Experimental Results of evaluating the Surface Roughness Ra during surface grinding for different coatings
Table 4. Experimental Results of evaluating Normal force Fn during surface grinding for different coatings
Experimental results GP Output
Depth
of cut
in µm (V1)
Grinding
speed
(V0)
Ra in µm
for PSZ
ground with Diamond
wheel
Ra in µm for
AT ground
with Diamond
wheel
Ra in µm for
AT ground
with CBN wheel
Ra in µm for A
ground with
Diamond wheel
Ra in µm for
A ground
with CBN wheel
Ra in µm
for PSZ
ground with CBN
wheel
PSZ using
CBN wheel
10 5 1 0.82 1.9 0.85 1 1.5 1.620467
10 10 0.9 0.82 1.7 0.8 0.8 1.4 1.449796
10 15 0.85 0.95 1.65 0.82 0.65 1.25 1.360538
10 30 0.8 0.9 1.7 0.81 0.64 1.15 1.253909
20 5 1.3 0.9 1.6 1.1 1.4 1.5 1.357232
20 10 1.1 0.84 1.6 1 1.3 1.35 1.348581
20 15 0.95 0.8 1.5 0.95 1.2 1.05 1.032365
20 30 0.7 0.85 1.4 0.79 1.1 1.2 1.209249
30 5 1.1 0.95 1.8 1.2 1.5 1.4 1.291292
30 10 0.98 0.77 1.5 1.1 1.4 1.2 1.209249
30 15 1 0.78 1.4 1 1.2 1.1 1.130757
30 30 0.62 0.73 1.3 0.71 1.15 1.25 1.360538
40 5 0.89 1 1.1 1.1 1.7 1.1 1.003651
40 10 0.75 0.75 0.9 1 1.6 1 1.000821
40 15 0.68 0.9 0.82 0.8 1.5 0.98 0.971932
40 30 0.63 0.6 0.73 0.7 1.4 0.93 0.891324
Experimental results GP Output
Depth of
cut in µm
(V1)
Grinding
speed in
m/sec (V0)
Fn in N for
PSZ using
diamond wheel
Fn in N for
AT using
diamond wheel
Fn in N for
AT using
CBN wheel
Fn in N for
A using
diamond wheel
Fn in N for
A using
CBN wheel
Fn in N for
PSZ using
CBN wheel
PSZ using
CBN wheel
10 5 0.5 2.5 4 2.8 4.2 3.9 4.074322
20 10 1.7 0.42 5.2 0.5 5.2 5.2 5.414451
30 15 0.7 0.55 5 0.6 5.5 5.5 5.40726
40 30 1.1 0.7 4.8 0.65 5.4 5.4 5.333083
10 5 1.25 2.5 4.1 2.7 4.6 4.1 3.996253
20 10 2 2.1 5.4 2.2 5.9 5.5 5.40726
30 15 1.3 0.6 5.8 0.7 6.5 6 5.923153
40 30 1.8 0.75 5.2 0.72 6.4 5.6 5.607811
10 5 1.3 3 4.8 3.3 5.3 4.7 4.746202
20 10 2.2 2.8 5.6 3.1 6 5.9 5.73641
30 15 1.5 0.7 6.2 0.78 6.6 6.4 6.407451
40 30 1.8 0.81 5.2 0.825 6.4 5.6 5.670387
10 5 1.7 3.8 5 3.9 5.6 4.9 4.962764
20 10 3 3.1 5.7 3.5 6.2 6.1 5.923153
30 15 1.8 0.9 6.5 1.1 7 6.75 6.853525
40 30 2 1.3 6.2 1.2 6.8 6.4 6.407451
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Table 5. Experimental Results of evaluating Tangential force Ft during surface grinding for different coatings
Experimental results
Depth
of cut
in µm ( V1)
Grinding
speed in
m/sec (V0)
Ft in N for
PSZ using
diamond wheel
Ft in N for
PSZ using
CBN wheel
Ft in N for
AT using
diamond wheel
Ft in N for
AT using
CBN wheel
Ft in N for
A using
diamond wheel
Ft in N for A
using CBN
wheel
10 5 0.2 0.3 0.18 0.4 0.2 0.6
20 10 0.215 0.5 0.25 0.62 0.35 0.8
30 15 0.24 0.6 0.14 0.8 0.3 0.9
40 30 0.29 0.55 0.255 0.75 0.21 0.85
10 5 0.24 0.4 0.145 0.6 0.3 0.42
20 10 0.29 0.65 0.33 0.85 0.4 0.65
30 15 0.35 0.71 0.31 1 0.35 0.75
40 30 0.4 0.69 0.41 0.95 0.24 0.7
10 5 0.35 0.56 0.21 0.8 0.44 0.95
20 10 0.38 0.8 0.34 1.2 0.47 1.5
30 15 0.54 1 0.21 1.4 0.45 1.8
40 30 0.5 0.98 0.46 1.3 0.4 1.6
10 5 0.45 0.75 0.22 1 0.53 0.4
20 10 0.5 1 0.52 1.5 0.57 0.62
30 15 0.67 1.4 0.23 1.8 0.53 0.8
40 30 0.62 1.2 0.55 1.6 0.68 0.75
Table 6. Experimental Results of evaluating surface roughness Ra after lapping for different coatings
4. GENETIC MODELS – RESULTS AND DISCUSSION
Using GP simulation, During Grinding operation, the Normal force Fn is given
by,
Where V2=
Hardness of coatings, V3=Toughness of coatings and V4=Hardness of Grinding
wheels (refer table 4.)
Experimental results GP Output
Depth
of cut
in µm
( V4)
Lapping
time in
minutes
( V1)
Ra in µm for
PSZ using
diamond
wheel
Ra in µm
for AT
using
diamond wheel
Ra in µm for
AT using
CBN wheel
Ra in µm
for A using
diamond
wheel
Ra in µm
for A using
CBN wheel
Ra in µm
for PSZ
using CBN
wheel
PSZ using
CBN wheel
0.25 5 0.5 0.38 0.4 0.72 0.72 0.53 0.54021
0.25 10 0.35 0.28 0.31 0.41 0.42 0.38 0.383549
0.25 15 0.28 0.22 0.24 0.33 0.35 0.3 0.293363
0.25 20 0.2 0.18 0.18 0.24 0.27 0.21 0.209842
0.25 25 0.18 0.15 0.16 0.23 0.25 0.18 0.186963
0.25 30 0.175 0.14 0.14 0.2 0.21 0.18 0.183273
8 5 0.75 0.49 0.5 0.8 0.8 0.7 0.681086
8 10 0.47 0.39 0.42 0.52 0.58 0.5 0.482033
8 15 0.33 0.29 0.31 0.44 0.48 0.36 0.36672
8 20 0.24 0.24 0.26 0.34 0.38 0.28 0.27575
8 25 0.21 0.2 0.205 0.31 0.35 0.22 0.224667
8 30 0.2 0.195 0.2 0.295 0.32 0.2 0.209842
14 5 0.82 0.65 0.68 0.905 0.98 0.88 0.847382
14 0 0.55 0.5 0.52 0.82 0.85 0.6 0.601887
14 15 0.45 0.4 0.43 0.62 0.65 0.51 0.507382
14 20 0.4 0.3 0.32 0.52 0.58 0.45 0.456926
14 25 0.33 0.28 0.3 0.46 0.5 0.38 0.383549
14 30 0.28 0.26 0.28 0.38 0.4 0.32 0.310341
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Using GP simulation, During Grinding operation, the Tangential force Ft is
given by,
Where V2= Hardness of coatings, V3=Toughness of
coatings and V4=Hardness of Grinding wheels (refer table 5.)
Using GP simulation, During Grinding operation, the surface roughness Ra is
given by,
Where V2= Hardness of coatings, V3=Toughness
of coatings and V4=Hardness of Grinding wheels (Refer table3.)
Using GP simulation, During Lapping operation, the surface roughness Ra is
given by
Where V0= Hardness of coatings, V3=Toughness
of coatings and V2=Hardness of Grinding wheels (refer table 6.)
The percentage deviation of GP (expected) and experimental results for
Normal grinding forces simulation of the Grinding Machining process are
shown on Figure 1 and the Tangential grinding forces are presented in Figure 2.
Whereas results of surface roughness Ra during grinding and lapping
operations are shown in figure 3 and figure 4 respectively. The Discipulus GP
technique was able to simulate these output variables within an average of
1.9% of their measured value, with no value exceeding a 5% deviation.
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Figure 1. Percentage deviation curve between the best models regarding individual generation and experimental results of Fn
Figure 2. Percentage deviation curve between the best models regarding individual generation and experimental results of Ft
Figure 3. Percentage deviation curve between the best models regarding individual generation and experimental results of Ra
value during Grinding
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Figure 4. Percentage deviation curve between the best models regarding individual generation and experimental results of Ra
value during Lapping
4 CONCLUSION
Oxide ceramics such as Alumina (A), Alumina-Titania (AT) and Partial
stablized zirconia (PSZ), have been widely used for many industrial
applications by deposition of thick film of hard materials on a relatively softer
substrate of the engineering components. Before deciding on the selection of
coatings, it is essential to look into its characteristics which also includes its
machinability for control of size and shape of end products. The above work
was taken up as product development of ceramic coated surfaces are having
immense value for industrial applications.
In this paper, the genetic programming was used for predicting the
machinability of ceramic materials. In the proposed concept the mathematical
models for verifying the machinability are subject to adaptation. After many
trials, with the help of validation and testing data, the fittest model reliability is
98%. Thus, in this case the reliability was almost 100%. Some of genetically
developed models of cutting parameters of precision machining of ceramic
oxide coatings, out of many successful solutions are presented here. The
accuracies of solutions obtained by GP depend on applied evolutionary
parameters and also on the number of measurements and the accuracy of
measurement. In general, more measurements supply more information to
evolution which improves the structures of models and we have provided
enough data.
In this paper, the genetic programming was used for predicting the
Machinabiliy characteristics for verifying the experimental results of Cutting
parameters which are subject to adaptation. Its reliability is approximately 98%
in the prediction of various outputs of results. In the testing phase, the
genetically produced model gives the same result as actually found out during
the experiment, with the reliability of almost cent percent. It is inferred from
our research findings that the genetic programming approach could be well
used for the prediction of Machinability characteristics of ceramic coatings
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),
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without conducting the experiments. This helps to establish efficient planning
and optimizing of process for the quality production of ceramic coatings
depending upon the functional requirements. Further work is on progress, for
the prediction/optimization of mechanical and tribological characteristics of
Industrial ceramic coatings by developing a mathematical model.
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